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Auditing ML systems and maintaining compliance readiness

Instruction and application
Complete

Can you prove it?​

Imagine being asked to prove your ML system is safe, fair, and compliant—on the spot. Could you do it?

That’s the challenge audits are designed for. They’re not just about finding mistakes—they’re about showing you’re ready, accountable, and in control. In this section, you’ll explore what audits cover and how to build a system that’s always audit-ready.

Microscope illustration

The role and scope of ML audits​

ML audits provide independent checks on whether your systems meet legal, ethical, and organisational standards. They examine the entire system:

  • Data legality: Are sources properly licensed and privacy-compliant?
  • Fairness: Were bias assessments conducted and documented?
  • Explainability: Is model behavior transparent to stakeholders?
  • Operational controls: Are security, access, and incident response protocols in place?

Key Point​

Audits validate that your system is trustworthy and aligned with values. They help build a culture of transparency and continuous improvement.

Audit preparation and required artefacts​

You need a paper trail to back up your claims. Key artefacts include:

  • Model Cards: Summaries of purpose, inputs, performance, and limitations.
  • Risk Registers: Lists of identified risks with mitigation owners and status.
  • Version Histories: Systematic records of model and dataset iterations (e.g., using MLflow).
  • Access Logs: Documentation of who accessed what data and when.
  • Compliance Self-Assessments: Internal reviews showing alignment with standards like the EU AI Act.

Sustaining audit readiness​

Compliance is an ongoing process, not a one-time checkbox.

  1. Compliance Monitoring Tools: Use dashboards to track accuracy and bias metrics.
  2. Automated Log Capture: Record access events and system changes automatically.
  3. Routine Reviews: Scheduled monthly or quarterly checkpoints to update risk registers.
  4. Stakeholder Communication: Build trust by sharing audit outcomes and compliance reports.

Action item: Pause and reflect​

Think about the small changes you can make today to improve your project's audit readiness.

Reflection: Audit Readiness
1. What kinds of documentation and artefacts would you need to prepare if your team were audited tomorrow?

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2. What small change could you make in your current ML project to improve audit readiness?

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